Project description:Most human transcripts are alternatively spliced, and many disease-causing mutations affect RNA splicing. Towards better modeling the sequence determinants of alternative splicing, we measured the splicing patterns of nearly 2 million (M) synthetic mini-genes, which include degenerate subsequences totaling to nearly 100M bases of variation. The massive size of these training data allowed us to improve upon current models of splicing as well as to gain new mechanistic insights. Our results show that a vast majority of hexamer sequence motifs measurably influence splice site selection when positioned within alternative exons, with multiple motifs acting additively rather than cooperatively. Intriguingly, motifs that enhance (suppress) exon inclusion in alternative 5’ splicing also enhance (suppress) exon inclusion in alternative 3’ or cassette exon splicing, suggesting a universal mechanism for alternative exon recognition. Finally, our empirically trained models are highly predictive of the effects of naturally occurring variants on alternative splicing in vivo.
Project description:Calcium is a critical signaling molecule in many cell types including immune cells. The calcium-release activated calcium channels (CRAC) responsible for store-operated calcium entry (SOCE) in immune cells are gated by STIM family members functioning as sensors of Ca2+ store content in the endoplasmic reticulum. We investigated the effect of SOCE blocker BTP2 on human peripheral blood mononuclear cells (PBMC) stimulated with the mitogen phytohemagglutinin (PHA).
Project description:When faced with potential threat we must estimate its probability, respond advantageously, and leverage experience to update future estimates. Threat estimation is the proposed domain of the forebrain, while behaviour is elicited by the brainstem. Yet, the brainstem is also a source of prediction error, a learning signal to acquire and update threat estimates. Neuropixels probes allowed us to record single-unit activity across a 21-region brainstem axis in rats receiving probabilistic fear discrimination with foot shock outcome. Against a backdrop of diffuse behaviour signaling, a brainstem network with a dorsal hub signaled threat probability. Neuronal function remapping during the outcome period gave rise to brainstem networks signaling prediction error and shock on multiple timescales. The results reveal brainstem networks construct threat probability, behaviour, and prediction error signals from neuronal building blocks.
Project description:Flax is an important oilseed crop in North America and is mostly grown as a fibre crop in Europe. As a self-pollinated diploid with a small estimated genome size of ~370 Mb, flax is well suited for fast progress in genomics. In the last few years, important genetic resources have been developed for this crop. Here, we describe the assessment and comparative analyses of 1,506 putative simple sequence repeats (SSRs) of which, 1,164 were derived from BAC-end sequences (BESs) and 342 from expressed sequence tags (ESTs). The SSRs were assessed on a panel of 16 flax accessions with 673 (58 %) and 145 (42 %) primer pairs being polymorphic in the BESs and ESTs, respectively. With 818 novel polymorphic SSR primer pairs reported in this study, the repertoire of available SSRs in flax has more than doubled from the combined total of 508 of all previous reports. Among nucleotide motifs, trinucleotides were the most abundant irrespective of the class, but dinucleotides were the most polymorphic. SSR length was also positively correlated with polymorphism. Two dinucleotide (AT/TA and AG/GA) and two trinucleotide (AAT/ATA/TAA and GAA/AGA/AAG) motifs and their iterations, different from those reported in many other crops, accounted for more than half of all the SSRs and were also more polymorphic (63.4 %) than the rest of the markers (42.7 %). This improved resource promises to be useful in genetic, quantitative trait loci (QTL) and association mapping as well as for anchoring the physical/genetic map with the whole genome shotgun reference sequence of flax.
Project description:Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying group-specific sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "group-specific" in our study. Our main purpose is to discover group-specific sequence regions between control and case groups as disease-associated markers. We developed a long k-mer (k ? 30 bps)-based computational pipeline to detect group-specific sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide de novo assembly. We called our method MetaGO: Group-specific oligonucleotide analysis for metagenomic samples. An open-source pipeline on Apache Spark was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified group-specific markers. In the simulated dataset, 99.11% of group-specific logical 40-mers covered 98.89% disease-specific regions from the disease-associated strain. In addition, 97.90% of group-specific numerical 40-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 group-specific 40-mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from ?0.8 to ?0.9) than that of previous studies. All group-specific 40-mers were present in LC patients, but not healthy controls. All the assembled 11 LC-specific sequences can be mapped to two strains of Veillonella parvula: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying group-specific k-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.
Project description:BackgroundRNA interference (RNAi) has been used as a promising approach to inhibit human immunodeficiency virus type 1 (HIV-1) replication for both in vitro and in vivo animal models. However, HIV-1 escape mutants after RNAi treatment have been reported. Expressing multiple small interfering RNAs (siRNAs) against conserved viral sequences can serve as a genetic barrier for viral escape, and optimization of the efficiency of this process was the aim of this study.ResultsAn artificial polycistronic transcript driven by a CMV promoter was designed to inhibit HIV-1 replication. The artificial polycistronic transcript contained two pre-miR-30a backbones and one pre-miR-155 backbone, which are linked by a sequence derived from antisense RNA sequence targeting the HIV-1 env gene. Our results demonstrated that this artificial polycistronic transcript simultaneously expresses three anti-HIV siRNAs and efficiently inhibits HIV-1 replication. In addition, the biosafety of MT-4 cells expressing this polycistronic miRNA transcript was evaluated, and no apparent impacts on cell proliferation rate, interferon response, and interruption of native miRNA processing were observed.ConclusionsThe strategy described here to generate an artificial polycistronic transcript to inhibit viral replication provided an opportunity to select and optimize many factors to yield highly efficient constructs expressing multiple siRNAs against viral infection.